Hadoop Ecosystem B Y R A H I M A.

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1 Hadoop Ecosystem B Y R A H I M A.

2 History of Hadoop Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open source web search engine, itself a part of the Lucene project. In 2003, Google published a paper described Google s distributed file system GFS. In 2004, Nutch s developers implemented open source version on GFS, the Nutch Distributed File System (NDFS) which evolved to HDFS In 2004, Google published the paper that introduced MapReduce In 2005, the Nutch developers implemented MapReduce in Nutch, and ported all the major Nutch algorithms to run using MapReduce and NDFS Thanks to Google, Hadoop including HDFS and MapReduce, was born in 2005

3 The Motivation for Hadoop In pioneer days they used oxen for heavy pulling, and when one ox couldn t budge a log, we didn t try to grow a larger ox. We shouldn t be trying for bigger computers, but for more systems of computers. Grace Hopper Main motivation for Hadoop is to utilize distributed systems for single job

4 Hadoop Vs. Traditional systems Traditional systems Data is stored in a central location Data is copied to processors at runtime Fine for limited amounts of data Hadoop Distribute data when the data is stored Run computation where the data is - bring the computation to the data Data is replicated Hadoop is scalable and fault tolerant

5 Impala HBASE Solr zookeeper Ambari Tez Flume Spark Pig Hive Oozie Sqoop Mahout Hadoop Related Project HUE MapReduce2 YARN HDFS (File types: Avro, Parquet, Snappy) ** Apart them there are also projects Storm, Kafka, Samza for streaming and message processing; Chukwa for data collection; Drill for interactive data analysis; Cascading, Flink, Whirl

6 Hadoop core components Hadoop common Common utilities to support other Hadoop modules Hadoop Distributed File System (HDFS) Distributed file system Yet Another Resource Negotiator (YARN) Framework for job scheduling and cluster resource manager MapReduce2 YARN based framework for parallel processing MapReduce1 Older and depreciated map reduce framework, based on own job scheduler forget about it!

7 HDFS Properties: Sits on top of a native filesystem - such as ext3, ext4 or xfs HDFS performs best with a modest number of large files millions, rather than billions, of files, Each file typically 100MB or more Files in HDFS are write once - no random writes to files HDFS is optimized for large, streaming reads of files rather than random reads HDFS deamons Datanode stores file blocks Namenode store filesystem s metadata (file name and path, number of blocks, number of block replicas, blocks locations) Secondary namenode bookkeeping and check points

8 HDFS: file storage

9 YARN Concepts YARN is cluster resource management system Provides APIs for requesting and working with cluster resources CPU and Memory YARN daemons Resource manager (one per cluster) - to manage the use of resources across the cluster Node managers - running on all the nodes in the cluster to launch and monitor containers. A container - executes an application-specific process with a constrained set of resources (memory, CPU) Application s history server only for map reduce jobs, stores job running histories

10 YARN features Scalability can run on larger clusters than MapReduce 1. It is designed to scale up to 10,000 nodes and 100,000 tasks. Availability provide HA for the resource manager, then for YARN applications Utilization In YARN, a node manager manages a pool of resources, rather than a fixed number of designated slots Multitenancy the biggest benefit of YARN is that it opens up Hadoop to other types of distributed application beyond MapReduce

11 YARN: how applications are run

12 MapReduce MapReduce is a method for distributing a task across multiple nodes Each node processes data stored on that node Where possible Consists of two phases: Map Reduce Features of MapReduce Automatic parallelization and distribution Fault tolerance A clean abstraction for programmers MapReduce programs are usually written in Java Can be written in any language using Hadoop Streaming MapReduce abstracts all the housekeeping away from the developer

13 MapReduce Key stages The Mapper Each Map task (typically) operates on a single HDFS block Map tasks (usually) run on the node where the block is stored Shuffle and Sort Sorts and consolidates intermediate data from all mappers Happens after all Map tasks are complete and before Reduce tasks start The Reducer Operates on shuffled/sorted intermediate data (Map task output) Produces final output

14 MapReduce Workflow

15 Motivation for Hadoop related projects Complexity of writing MapReduce jobs Not all tasks fits MapReduce it is not good for complex directed-acyclic-graphs It is not suitable for iterative algorithms Address business and data analyst needs Performance issues Data ingestion and integration with traditional systems Provide ready to use frameworks and system build on big data

16 HIVE Apache Hive is a high level abstraction on top of MapReduce Uses an SQL like language called HiveQL Generates MapReduce jobs that run on the Hadoop cluster Originally developed by Facebook for data warehousing Hive Limitations Not all standard SQL is supported No correlated subqueries No support for UPDATE or DELETE No support for INSERTing single rows

17 PIG Apache Pig is a platform for data analysis and processing on Hadoop Offers an alternative to writing MapReduce code directly Originally developed at Yahoo Pig Goals: flexibility, productivity, and maintainability Main components: The data flow language (Pig Latin) The interactive shell (Grunt) The Pig interpreter and execution engine

18 High performance SQL engine for vast amounts of data Similar query language to HiveQL 10 to 50+ Ames faster than Hive, Pig, or MapReduce Developed by Cloudera MapReduce is not optimized for interactive queries -High latency even trivial queries can take 10 seconds or more Impala does not use MapReduce Uses the same Metastore as Hive Impala

19 SQOOP and Flume Sqoop Sqoop stands for SQL for Hadoop Imports tables from an RDBMS into HDFS and vice-versa Just one table All tables in a database Sqoop supports a WHERE clause Uses MapReduce to actually import the data to RDBMS Uses a JDBC interface Supports Oracle Database (connector developed with Quest Software) Flume Flume is a distributed, reliable, available service for efficiently moving large amounts of data as it is produced Ideally suited for file ingestions (web logs, network device logs etc.)

20 Sample Workflow

21 Oozie Oozie is a workflow engine Runs on a server, typically outside the cluster Runs workflows of Hadoop jobs Including Pig, Hive, Sqoop jobs Submits those jobs to the cluster based on a workflow definition Workflow definitions are in XML and submitted via HTTP Jobs can be scheduled One-off or recurring jobs

22 HBASE HBase is the Hadoop database - A NoSQL datastore Developed as open source version of Google s BigTable Can store massive amounts of data Petabytes+ High write throughput Scales to 100K inserts per second Handles sparse data well No wasted spaces for empty columns in a row Limited access model Optimized for lookup of a row by key rather than full queries No transactions: single row operations only Only one column (the row key ) is indexed

23 HBASE vs RDBMSs RDBMS HBASE Data layout Row oriented Column oriented Transaction Supported Single row only Query language SQL Put/get/scan Security Buit-in Authentication- Authorization Kerberos Indexes Any column Row key only Max data size TBs PB+ Read-write throughput Thousands Millions

24 Spark Spark is an open source project, started in 2009 as a research project in the UC Berkeley Spark is a cluster computing platform designed to be fast and general-purpose times faster than MapReduce so it is MapReduce killer Written in Scala, using Akka framework Spark can run as standalone cluster, on YARN, and on Apache Mesos cluster Spark components: Spark SQL Spark Streaming real-time ML Machine learning library GraphX Graph processing Spark core Standalone YARN Mesos

25 Spark components Spark Core task scheduling, memory management, fault recovery, interacting with storage systems, and more. API that defines resilient distributed datasets (RDDs), - RDDs represent a collection of items distributed across Spark cluster nodes that can be manipulated in parallel. Spark SQL Spark s package for working with structured data Spark Streaming Spark component that enables processing of live streams of data MLlib Provides classification, regression, clustering, collaborative filtering GraphX Library for manipulating graphs and performing graph-parallel computations

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